Method and image reconstruction device for reconstructing image data

ABSTRACT

A method and an image reconstruction device are disclosed for reconstructing image data on the basis of input projection data obtained via an X-ray computerized tomography system. A target convolutional kernel is selected, which, when reconstructing image data from the input projection data using simple filtered back projection, would lead to target image characteristics. Image data is then reconstructed using an iterative reconstruction method of at least one embodiment. In at least one embodiment, the method includes a) reconstructing image data of a first iterative stage from the input projection data, b) generating synthetic projection data on the basis of the image data of the current iterative stage, c) forming difference projection data on the basis of the input projection data and the synthetic projection data, d) generating residue image data from the difference projection data, e) combining the residue image data with the image data of the current iterative stage to form image data of an additional iterative stage, wherein the image data of the current iterative stage is subjected to filtering before or during combination with the residue image data by using a regularization convolutional kernel which is determined on the basis of the selected target convolutional kernel, and f) repeating b) to e) until a termination condition occurs.

PRIORITY STATEMENT

The present application hereby claims priority under 35 U.S.C. §119 onGerman patent application number DE 10 2009 014 726.8 filed Mar. 25,2009, the entire contents of which are hereby incorporated herein byreference.

FIELD

At least one embodiment of the invention generally relates to a methodfor reconstructing image data on the basis of input projection dataobtained by way of an x-ray computerized tomography system. In at leastone embodiment, it relates to a method wherein a target convolutionalkernel is selected, which, when reconstructing image data from the inputprojection data using simple filtered back projection, would lead totarget image characteristics. At least one embodiment of the inventionalso generally relates to a method for generating image data from insidean object, wherein a reconstruction method of this type is used. Atleast one embodiment of the invention also generally relates to an imagereconstruction device for reconstructing image data using a method ofthis type and to an x-ray computerized tomography system having aprojection data acquisition unit and a corresponding imagereconstruction device.

BACKGROUND

These days what is known as a filtered back projection (FBP) method isused as the standard method for reconstructing CT scanned image datafrom X-ray CT data records of a computerized tomography device (CTdevice). In this method, the projection measured data acquired from thecomputerized tomography scanner is conventionally firstly pre-processedin order to free it as far as possible from noise. What is known as a“rebinning” step is then carried out in which the data generated withthe beam that spreads from the source in a fan-shaped manner isrearranged such that it is in a form as if the detector had been struckby an X-ray wave front tapering parallel to the detector.

The data is then transformed into the frequency range. Filtering takesplace in the frequency range by using a convolutional kernel which inmost devices the operator can nowadays freely select from a menu usingthe user interface. Up to 80 different convolutional kernels arecurrently offered on some systems. The user can influence the imagecharacteristics via the choice of convolutional kernel. Imagecharacteristics include not just the image definition but also, forexample, the image noise, granularity, texture and behavior inlow-frequency bands, etc.

The user can therefore select for example whether he wants toreconstruct a very soft image or a very sharp image which, however, hasgreater granularity. The operator's selection can depend inter alia onthe measuring situation, for example on which region of the imagesshould be particularly well depicted and according to which objects orlesions are being sought. The filtered data is then inverselytransformed. Using the data that has been re-sorted and filtered in thisway back projection then takes place to the individual voxels within thevolume of interest.

Owing to its approximative mode of operation problems with what arereferred to as low-frequency cone beam artifacts and spiral artifactsoccur with the conventional FBP methods, however. Furthermore, imagedefinition is always linked to image noise in the case of conventionalFBP methods. The greater the sharpness achieved, the higher the imagenoise also is, and vice versa.

Iterative reconstruction methods have therefore recently been developedwith which these limitations may be eliminated. With such iterativereconstruction methods initial image data is firstly reconstructed fromthe projection measured data. A convolutional back projection method forexample can be used for this purpose.

Synthetic projection data is then generated from this initial image datausing a “projector” (projection operator), which is designed tomathematically depict the measuring system as well as possible. Thedifference from the measured signals is then back-projected with theadjoint operator, and a residue image is thus reconstructed with whichthe initial image is updated. The updated image data can in turn be usedto generate new synthetic projection data in a next iterative step usingthe projection operator, form therefrom the difference from the measuredsignals again and calculate a new residue image with which the imagedata of the current iterative stage is improved again, etc. Using thistype of method it is possible to reconstruct image data that hasrelatively good definition but still has low image noise.

One drawback of this iterative method in contrast to the simple backprojection mentioned in the introduction, however, lies in the fact thatthe operator no longer has any direct influence on the imagecharacteristics. In iterative reconstruction the image characteristicsare influenced by the projector and the associated back projector used,and by what is known as the regularization term with which the grayscale values of adjacent image voxels are weighted with a potentialfunction within iteration in order to achieve sufficient stability inthe reconstruction. It is unclear in this connection how the differentcomponents need to be parameterized in detail to achieve certain imagecharacteristics.

SUMMARY

In at least one embodiment of the present invention, a method forgenerating image data of an object and a corresponding imagereconstruction device in which the image characteristics can be easilyoptionally adjusted by the operator in a manner similar to the choice ofCT convolutional kernel.

In the case of at least one embodiment of the inventive method a type of“virtual” target convolutional kernel is firstly selected, i.e. a“desired convolutional kernel”, which, when reconstructing image datafrom the input projection data, would lead to target imagecharacteristics if it was used with simple filtered back projection.

The iterative reconstruction method itself, of at least one embodiment,then comprises the following:

a) Reconstructing image data of a first iterative stage from the inputprojection data. This means initial image data is firstly generated fromthe input projection data using conventional convolutional backprojection by way of example.

b) Synthetic projection data is then generated on the basis of the imagedata of the current iterative stage (in the case of the first iterativestage from the initial image data accordingly). As described in theintroduction, a projection operator that depicts the measuring processas well as possible is used for this purpose.c) Difference projection data is then generated on the basis of theinput projection data and the synthetic projection data of the currentiterative stage. The difference projection data is therefore a measureof the deviation of the projection data that can be generated from thecurrent image data from the actual input projection data, and thereforea measure of the quality of the generated image data of the currentiterative stage.d) Reside image data is then generated from the difference projectiondata.e) This residue image data is finally combined with the image data ofthe current iterative stage to form image data of an additionaliterative stage.

According to at least one embodiment of the invention the image data ofthe current iterative stage is in the process subjected to filteringbefore or during combination with the residue image data by using aregularization convolutional kernel which is determined on the basis ofthe selected target convolutional kernel. In other words, theregularization term used in iteration is accordingly influenced by theregularization convolutional kernel, and therefore by the targetconvolutional kernel as well, in such a way that image characteristicsare ultimately formed in the convergence image of iteration which matchthe target image characteristics determined by the selected targetconvolutional kernel.

Steps b) to e) of at least one embodiment of this iterative method arerepeated until a termination condition occurs. The termination conditioncan, for example, be selected such that iteration is terminated at thelatest after a predefined number of iterative steps. Alternatively it isalso possible to carry out iteration as a function of achieving aconvergence criterion. Iteration can preferably be terminated if thedifference projection data or the residue image data satisfy a thresholdcriterion.

With the aid of at least one embodiment of the inventive method the useris therefore provided with the possibility, as before, of specifying atarget convolutional kernel and of thereby influencing the imagecharacteristics. The advantageous iterative method can still be used tothus produce for example independence between image definition and imagenoise even within certain limits. Overall a significant improvement inreconstruction can therefore be attained.

In the case of at least one embodiment of the inventive method forgenerating image data from inside an object by way of an x-raycomputerized tomography system, the object is x-rayed using x-rayradiation from a plurality of projection directions to acquireprojection measured data. Reconstruction is then carried out on thebasis of the projection measured data using the above-describedreconstruction method of at least one embodiment. The projectionmeasured data can, for example, firstly be pre-processed to generate theinput projection data therefrom for at least one embodiment of theinventive method. Thus, for example, the projection measured data canfirstly be filtered as usual and be freed from noise as far as possibleand then a rebinning step can optionally also be carried out asdescribed above. It is also possible for the projection measured data tobe interpolated on the basis of actually measured detector projectionmeasured data in this connection.

The projection measured data can be acquired in various ways, i.e. in asequential method as well as in a helical method. The image data cansimilarly be reconstructed in various ways. For example individualsection images can be reconstructed in a sequential method and these arethen combined to form volume image data, or volume image data isreconstructed in a helical method from which individual section imagescan then also be generated.

A corresponding image reconstruction device for reconstructing imagedata according to at least one embodiment of the inventive method musthave a projection measured data interface for accepting input projectiondata obtained by means of an X-ray computerized tomography system.

It also requires a target convolutional kernel selection unit forselecting a target convolutional kernel, which, when reconstructingimage data from the input projection data using simple filtered backprojection, would lead to certain image characteristics. This targetconvolutional kernel selection unit can be, for example, a userinterface for direct selection of the target convolutional kernel by theuser. Basically it can, however, also be a unit which, for example bytaking account of various input parameters that characterize themeasuring situation, automatically selects a suitable targetconvolutional kernel. Such input parameters that describe the measuringsituation can be information about which object being examined is beingphotographed, for example whether it is a head photograph or anabdominal photograph, whether contrast medium is being used, whichstructures are being specifically sought, etc. Similarly, the operatorcould also choose desired image characteristics in a different way, forexample from a selection of offered image characteristics. A targetconvolutional kernel linked to these image characteristics and storedfor example in a table, is then automatically selected.

At least one embodiment of the inventive image reconstruction devicealso needs an iterative reconstruction unit which is adapted toreconstruct image data based on the input projection data using aniterative reconstruction method, and to use a reconstruction method asis described above in the process.

Finally the image reconstruction device requires an image data interfacefor outputting the reconstructed image data.

An image reconstruction device of this kind may be part of acomputerized tomography system, i.e. it can, for example, beconventionally installed on a control and evaluation computer in thetomography system. Basically an image reconstruction device of this kindcan also be implemented in the form of or on a different computer unit,however, which, for example, is connected for data transfer to acomputerized tomography system via a network or can be provided withcorresponding data in some other way.

In particular the iterative construction unit and optionally a targetconvolutional kernel selection unit can be implemented for automaticselection of a target convolutional kernel—as software modules on asuitable computer with corresponding storage capacities respectively.The raw data interface and the image data interface can also beimplemented in the form of pure software if it is only necessary toaccept the projection measured data or an image data output from or onother raw data pre-processing units or image data processing unitsimplemented on the same computer unit. Basically these interfaces can,however, also be implemented as combined hardware/software interfaces inorder to achieve external input/output, for example hardware interfacesspecially configured with the aid of software components. Output of CTscanned image data should be taken to mean any output of CT scannedimage data by the image reconstruction device, for example storing ofimage data in a memory for subsequent visual inspection or furtherprocessing, as well as external output onto a screen, printer or thelike.

An implementation that is largely software-based has the advantage thateven image reconstruction devices that have been used previously can beeasily retrofitted by way of a software update in order to work in theinventive way. In this respect the object is also achieved by a computerprogram product which can be directly loaded into a memory of aprogrammable image reconstruction device, comprising program steps toexecute all steps of the inventive method if the program is executed inthe image reconstruction device.

Further advantageous embodiments and developments of the inventionemerge from the further dependent claims and the description thatfollows. An inventive image reconstruction device of at least oneembodiment can also be developed analogously to the method.

As already mentioned above, the regularization convolutional kernel ispreferably determined by taking account of a projection operator used inabove-mentioned step b) when generating the synthetic projection data.

The residue image data is, moreover, preferably generated from thedifference projection data by means of a filtered back projection methodby using a predefined back projection convolutional kernel. Theregularization convolutional core is then also determined by takingaccount of this back projection convolutional kernel. The backprojection convolutional kernel used within iteration is particularlypreferably predefined such that optimally high definition is achieved.What is known as a “RamLak kernel” (the name RamLak originates from thediscoverers of this convolutional kernel, Ramachandran andLakshminraynan), or a Shepp-Logan kernel (named after the developers ofthis convolutional kernel, Shepp and Logan) is expedient for thispurpose.

In a further example variant of at least one embodiment of the inventivemethod separate material type image data for different types of materialrespectively is firstly generated from the image data of the currentiterative stage before combination with the residue image data. By wayof example, images can be generated for different contrast stages ormaterials, in particular it is possible to generate a bone image, i.e. ahigh-contrast image, and, furthermore, a soft tissue or water imagewhich exhibits only lower contrasts. This material type image data isthen subjected to separate filtering using a regularizationconvolutional kernel determined on the basis of the selected targetconvolutional kernel for the relevant type of material respectively.Only then does combination with the residue image data and alsooptionally with the image data from the current iterative stage takeplace. It is also particularly possible for filtering to be carried outfor only one of the types of material, for example for the soft tissueimage, i.e. a regularization convolutional kernel is only determined forthis type of material. The image data of the other type of material, forexample the bone image, can be combined unfiltered with the residueimage data and with the filtered material type image data of the othertype of material.

For this purpose the iterative reconstruction unit preferably comprisesa filter unit with a separating unit which is adapted to generateseparate material type image data for different types of material fromthe image data of the current iterative stage, before combination withthe residue image data, and to separately filter this material typeimage data by using a regularization convolutional kernel determined forthe respective type of material, before it is then combined with theresidue image data again.

Basically it is also possible to provide separation into more than twodifferent types of material, wherein care should be taken that theimprovement in image quality is gained by correspondingly highercalculational effort.

In a further preferred variant local weighting can take place whencombining the material type image data with the residue image data.Basically, however, all image data may also be weighted equally.

The input projection data is in each case preferably obtained on thebasis of projection measured data acquired by way of the X-raycomputerized tomography system, the data firstly being subjected to abeam hardening correction. This beam hardening correction can bedirectly applied to the measured data. In a preferred variant it can,however, also be carried out on the data that has already beenpre-processed, i.e. in particular subjected to rebinning. The effect ofwhat is referred to as “beam hardening” occurs as, on the one hand, theradiation emitted by an x-ray source has a polychromatic spectrum and,on the other hand, the absorption of the X-rays in the examined objectis energy-dependent. This leads to shifting of the mean energy of thex-ray radiation to higher values as a function of which material isbeing radiographed and how thick the material is. The longer theradiographed section in the body, the more intense the beam hardening.The beam hardening effect leads to undesirable image artifacts in thereconstructed image of the radiographed body layer and these can affectprecise medical interpretation of an image in particular. Variousalgorithms are known for correcting such beam hardening-inducedartifacts. The drawback of these methods again lies in the fact thatback projection is used in conjunction with a normal convolutionalkernel in this case as well and can reduce image definition. By havingbeam hardening precede iteration it may be ensured that the low-passfiltering induced within the framework of the beam hardening correction,i.e. image definition reduction, can be compensated for again within theiterative reconstruction.

In a particularly preferred example embodiment interim image data, i.e.provisional image data, is reconstructed from the projection measureddata, or the projection measured data that has been pre-processed asdescribed above, for the beam hardening correction. This can take placeusing a conventional convolutional back projection. First structureimage data of a first type of material is then segmented from thisinterim image data. This can be, for example, structure image data of acertain tissue type, for example of the bone structure, or image datawith which a contrast medium should be associated. Conventionalsegmenting methods can be used for this purpose.

On the basis of this first structure image data first structureprojection data can then be generated by a forward projection. This isprojection data on whose basis it would be possible to reconstruct thefirst structure image data, i.e. ultimately synthetic projection data isgenerated which would be measured if only the relevant structure of thetype of material selected in each case were to be present in the beampath. The beam hardening-corrected projection data can then bedetermined on the basis of this first structure projection data and theactually measured projection measured data.

To carry out this preferred method variant the beam hardening correctionunit preferably comprises an interim image reconstruction unit which isadapted to reconstruct interim image data from the projection measureddata. The beam hardening correction unit also comprises a segmentingunit which is constructed to segment first structure image data of apredefined first type of material from the interim image data, and aprojection data-generating unit which is adapted to generate firststructure projection data on the basis of the first structure imagedata. The actual correction unit is then connected downstream and isadapted to determine the beam hardening-corrected projection data on thebasis of the first structure projection data and the projection measureddata.

Beam hardening-corrected second structure projection data of apredefined second type of material is particularly preferably determinedon the basis of the first structure projection data and the projectionmeasured data. This second type of material can, for example, be adifferent tissue type, preferably soft tissue. Alternatively secondstructure projection data is determined for a type of material similarto the relevant soft tissue, for example water. This second structureprojection data is projection data on whose basis it would be possibleto reconstruct second structure image data, i.e. these are syntheticmeasured values which would have been measured if only structures of thesecond type of material had been present during projection in the beampath. The first structure projection data and the second structureprojection data can then be suitably combined while forming the beamhardening-corrected projection data.

A correction data table, which has been created on the basis ofmeasurements and/or simulations for various material thicknesscombinations for example, can preferably be used for beam hardeningcorrection.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be described again in more detail hereinafter withreference to the accompanying figures and with the aid of exampleembodiments. In the drawings:

FIG. 1 shows a schematic diagram of an example embodiment of acomputerized tomography system having an image reconstruction device,

FIG. 2 shows a schematic diagram of a first example embodiment of aniterative reconstruction unit for an inventive image reconstructiondevice having a target convolutional kernel selection unit and adepiction of the interaction between the individual components and therespective output and input data,

FIG. 3 shows a schematic diagram of an example embodiment of aninventive image reconstruction device with a depiction of theinteraction between the individual components and the respective outputand input data,

FIG. 4 shows a schematic diagram of an example embodiment of a beamhardening correction unit for an image reconstruction device accordingto FIG. 3 with a depiction of the interaction between the individualcomponents and the respective output and input data,

FIG. 5 shows a schematic diagram of a filter unit with a separation unitfor a second example embodiment of an iterative construction unit for aninventive image reconstruction device with a depiction of theinteraction between the individual components and the respective outputand input data,

FIG. 6 shows a diagram of a bone image and soft tissue image segmentedfrom a section image through a head.

DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTS

Various example embodiments will now be described more fully withreference to the accompanying drawings in which only some exampleembodiments are shown. Specific structural and functional detailsdisclosed herein are merely representative for purposes of describingexample embodiments. The present invention, however, may be embodied inmany alternate forms and should not be construed as limited to only theexample embodiments set forth herein.

Accordingly, while example embodiments of the invention are capable ofvarious modifications and alternative forms, embodiments thereof areshown by way of example in the drawings and will herein be described indetail. It should be understood, however, that there is no intent tolimit example embodiments of the present invention to the particularforms disclosed. On the contrary, example embodiments are to cover allmodifications, equivalents, and alternatives falling within the scope ofthe invention. Like numbers refer to like elements throughout thedescription of the figures.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, these elements should notbe limited by these terms. These terms are only used to distinguish oneelement from another. For example, a first element could be termed asecond element, and, similarly, a second element could be termed a firstelement, without departing from the scope of example embodiments of thepresent invention. As used herein, the term “and/or,” includes any andall combinations of one or more of the associated listed items.

It will be understood that when an element is referred to as being“connected,” or “coupled,” to another element, it can be directlyconnected or coupled to the other element or intervening elements may bepresent. In contrast, when an element is referred to as being “directlyconnected,” or “directly coupled,” to another element, there are nointervening elements present. Other words used to describe therelationship between elements should be interpreted in a like fashion(e.g., “between,” versus “directly between,” “adjacent,” versus“directly adjacent,” etc.).

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of exampleembodiments of the invention. As used herein, the singular forms “a,”“an,” and “the,” are intended to include the plural forms as well,unless the context clearly indicates otherwise. As used herein, theterms “and/or” and “at least one of” include any and all combinations ofone or more of the associated listed items. It will be furtherunderstood that the terms “comprises,” “comprising,” “includes,” and/or“including,” when used herein, specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

It should also be noted that in some alternative implementations, thefunctions/acts noted may occur out of the order noted in the figures.For example, two figures shown in succession may in fact be executedsubstantially concurrently or may sometimes be executed in the reverseorder, depending upon the functionality/acts involved.

Spatially relative terms, such as “beneath”, “below”, “lower”, “above”,“upper”, and the like, may be used herein for ease of description todescribe one element or feature's relationship to another element(s) orfeature(s) as illustrated in the figures. It will be understood that thespatially relative terms are intended to encompass differentorientations of the device in use or operation in addition to theorientation depicted in the figures. For example, if the device in thefigures is turned over, elements described as “below” or “beneath” otherelements or features would then be oriented “above” the other elementsor features. Thus, term such as “below” can encompass both anorientation of above and below. The device may be otherwise oriented(rotated 90 degrees or at other orientations) and the spatially relativedescriptors used herein are interpreted accordingly.

Although the terms first, second, etc. may be used herein to describevarious elements, components, regions, layers and/or sections, it shouldbe understood that these elements, components, regions, layers and/orsections should not be limited by these terms. These terms are used onlyto distinguish one element, component, region, layer, or section fromanother region, layer, or section. Thus, a first element, component,region, layer, or section discussed below could be termed a secondelement, component, region, layer, or section without departing from theteachings of the present invention.

FIG. 1 firstly schematically shows a computerized tomography system 1with an image reconstruction unit 21, this being an inventive imagereconstruction unit with a target convolutional kernel selection unit50.

The CT system 1 substantially comprises a conventional scanner 10 inwhich, on a gantry 11, a detector system 5 with a detector 16 and anX-ray source 15 located opposite the detector 16 revolves around ameasuring chamber 12. A patient-bearing device 3 or a patient couch 3,the upper part 2 of which, with a patient O located thereon, can bedisplaced toward the scanner 10, is situated upstream of the scanner 10to move the patient O through the measuring chamber 12 relative to thedetector system 16. The scanner 10 and the patient couch 3 arecontrolled by a controller 20 from which acquisition control signals ASemanate via a conventional control interface 24 to conventionallycontrol the entire system according to predefined measuring protocols.The movement of the patient O in the z direction, which matches thesystem axis z lengthwise through the measuring chamber 12, and thesimultaneous revolution of the X-ray source 15 produce a helical pathfor the X-ray source 15 relative to the patient O during measurement.The detector 16 runs concurrently therewith and always opposite theX-ray source 15 in order to capture projection measured data p_(m) whichis then used in the manner according to an embodiment of the inventionto reconstruct volume image data.

A sequential measuring method may also be carried out in which a fixedposition in the z direction is approached and then the requiredprojection measured data p_(m) is captured at the relevant z positionduring a revolution, partial revolution or a plurality of revolutions inorder to reconstruct a section image at this z position or toreconstruct volume image data from the projection data from a pluralityof z positions. Embodiments of the inventive method can basically alsobe used in other CT systems, for example with a plurality of X-raysources and/or detectors and/or with a detector that forms a completering.

The projection measured data p_(m) (also called raw data hereinafter)acquired from detector 16 is passed via a raw data interface 23 to thecontroller 20. This raw data, optionally following suitablepre-processing in the above-described manner, is then processed furtherin an image reconstruction device 21 which in this example embodiment isimplemented in the controller 20 in the form of software on a processor.This image reconstruction device 21 will be described in more detailhereinafter with reference to FIGS. 2 to 6.

The image data f reconstructed by the image reconstruction device 21 isthen stored in a memory 22 of the controller 20 and/or conventionallyoutput on the screen of the controller 20. It may also be fed via aninterface (not shown in FIG. 1) into a network connected to thecomputerized tomography system, for example a radiological informationsystem (RIS), and be stored in a mass storage device that can beaccessed there, or be output as images on printers or filming stationsthat are connected there. The data may thus be further processed in anydesired manner and is then stored or output.

The mathematical basis for carrying out the iterative reconstructionthat is executed in the image reconstruction device 21 will firstly bedescribed hereinafter:

During iteration the image data f_(k) is updated in each iterative stagek=0, 1, 2, . . . . This may be described by the following equation:f _(k+1) =f _(k)+α·grad_(f)(z)  (1)

The parameter α designates a relaxation parameter which controls thespeed of convergence. This preferably has the value 0.7. A differentvalue, preferably in the range from 0 to 1, may also be selected,however.

The term grad_(r)(z) is the gradient of what is known as the targetfunction x in the attenuation distribution f (i.e. the actual image dataf) which is given by the equationz(f)=∥Af−p _(c)∥_(K) ² +R(f)  (2)A is the projection operator used. It is selected such that the realmeasuring process is well depicted mathematically and is thereforepredefined as a function of the measuring process, and in particular themeasuring system. p_(c) represents the input projection data.

The scalar product in the first part of the sum in equation (2) isdefined as follows:∥Af−p _(c)∥_(K) ²=(Af−p _(c))^(T) ·K·(Af−p _(c))  (3)

The operator K is a convolutional kernel here which describes theconvolution of the projection data. K should be selected such thatoptimally high definition is achieved in the back projection. It istherefore also fixed. The term Af describes the operator A, applied tothe image data f, and therefore matches the synthetic projection datap_(s) shown in FIG. 2.

R(f) in equation (2) is the regularization term which is given byequation

$\begin{matrix}{{R(f)} = {\beta \cdot {\sum\limits_{i,j}^{N}{d_{i,j} \cdot {V\left( {f_{i} - f_{j}} \right)}}}}} & (4)\end{matrix}$V is, for example, a quadratic potential function with which the grayscale differences of adjacent image voxels at a spacing of 1/d_(i,j) areweighted. Other functions or weightings are also possible, however. iand j are control variables which each extend via the number N of voxelspresent in an image. β designates the regularization intensity thatregulates the amount of the regularization term with respect to thecorrection image in the k^(th) iteration. Preferred values for β liebetween 1.5 and 2.5. As already mentioned, the stability of thereconstruction is enforced by this regularization term R(f). As theregularization term R(f) may also be regarded in mathematical terms as aconvolutional kernel, it is also called regularization convolutionalkernel R here.

Overall grad_(f)(z) therefore results in:

$\begin{matrix}{{{grad}_{f}(z)} = {{{2 \cdot A^{T}}{K\left( {{Af} - p_{c}} \right)}} + {\beta{\sum\limits_{i = 1}^{N}{e_{i}{\sum\limits_{j = 1}^{N}{d_{i,j} \cdot \frac{\mathbb{d}{V\left( {f_{i} - f_{j}} \right)}}{\mathbb{d}f}}}}}}}} & (5)\end{matrix}$e_(i) designates the i^(th) unit vector in the image space herein, i.e.e_(i)=(0, . . . , 0, 1, 0, . . . , 0). In mathematical terms theiterative reconstruction within the framework of a steepest descentmethod therefore leads to a minimization of the target function z,defined by equation (2), of the attenuation distribution f or image dataf.

It may be shown that the reconstruction for k→∞ formulated by the aboveequations converges against the convergence imagef _(∞)=(A ^(T) ·K·A+βR)⁻¹ ·A ^(T) ·K·p _(c)  (6)(J. Sunnegårdh, “Combining Analytical and Iterative Reconstruction inHelical Cone-Beam-CT”, Thesis No. 1301, Linköpink Studies in Science andTechnology, 2007, the entire contents of which are hereby incorporatedherein by reference).

Equation (6) is unlimited if the potential function V is quadratic andwhat is known as the “Influence Function” dV/df is linear. In this casethe iteration equation (1) is a linear depiction of the image data f.Furthermore, equation (6) also applies whenever there are onlyrelatively small contrasts in the image data, for example in the case ofsoft tissue image data, in which the adjustment of the imagecharacteristics is particularly important. In this case the “InfluenceFunction” dV/df is independent of the specific choice of the potentialV, i.e. even if V is not quadratic, dV/df can be at least locallylinearized. The inventive method can therefore also expediently be usedif the potential function V was, by way of exception, not selected to bequadratic.

According to an embodiment of the invention a target convergence imagef_(∞) should be achieved which has image characteristics, which, whenreconstructing image data from the input projection data p_(c) by way ofsimple filtered back projection, would be achieved with a specifictarget or desired convolutional kernel W. This means the convergenceimage should satisfy the following condition:f _(∞) =A ^(T) ·W·p _(c)  (7)Equating equations (6) and (7) results in(A ^(T) ·K·A+βR)⁻¹ ·A ^(T) ·K=A ^(T) ·W  (8)

The variable p_(c) in the two equations (6) and (7) for the inputprojection data could be eliminated here, so equation (8) is independentof input projection data p_(c). It can immediately be seen from equation(8) that with a given operator A, a given convolutional kernel K can becalculated for simple back projection within the iteration loop, andwith a given regularization intensity β of the sought regularizationconvolutional kernel R as a function of a target convolutional kernel Wselected by a user.

The actual procedure when calculating within the framework of theiterative reconstruction may be depicted more easily using an example inthe frequency domain. It is assumed in this connection that thereconstruction A^(T)K is a WFBT reconstruction (WFBT=Weighted FilteredBack Projection), as is described in the article “Weighted FBP—a simpleapproximate 3D FBP algorithm for multislice spiral CT with good doseusage for arbitrary pitch” by Karl Stiersdorfer, Annabella Rauscher, JanBoese, Herbert Bruder, Stefan Schaller and Thomas Flor in Phys. Med.Biol. 49 (2004), 2009-2218, the entire contents of which are herebyincorporated herein by reference. This is therefore a filtered backprojection with a specific kernel K and a voxel-driven 3D backprojection with bilinear interpolation.

When using a very fine parallel grid a, which matches the spacings ofthe detector channels, and a pixel width s, equation (8) may betransformed as follows to obtain a ratio for the modulation transferfunctions:sin c ²·(π·ρ·a)·M _(W)=(sin c ²·(π·ρ·a)·M _(W)·sin c ²·(π·ρ·s)+βM_(R))⁻¹·sin c ²·(π·ρ·a)·M _(W)  (9)

In this equation the term sin c²·(π·ρ·a) corresponds to a linearinterpolation within the back projection step and sin c²·(π·ρ·a)corresponds to the linear interpolation in forward projector A. Thefactor M_(K) corresponds to the convolutional kernel K, factor M_(W) toconvolutional kernel W and factor M_(R) to the regularizationconvolutional kernel R. This means multiplication by the respectivefactor in the frequency domain corresponds to the convolution with therespective convolutional kernel in the position space.

Equation (9) can then be resolved according to M_(R). The followingequation is produced in a special case where a kernel K is selected suchthat M_(K)=1, for example when using a RamLak kernel:

$\begin{matrix}{M_{R} = {\frac{1}{\beta} \cdot \left( {{\frac{1}{M_{W}} \cdot \sin}\;{c^{2} \cdot \left( {\pi \cdot \rho \cdot a} \right) \cdot \sin}\;{c^{2} \cdot \left( {\pi \cdot \rho \cdot s} \right)}} \right)}} & (10)\end{matrix}$In other cases equation (10) looks a little more complicated. In thepractical case M_(R) may easily be calculated in this case as well,however.

If factor M_(R) is known, then, using this factor in each iterative stepaccording to equation (1), grad_(f)(z) can be calculated according toequation (5), where equation (5) reads as follows in a slightlydifferent notation with regularization convolutional kernel R:grad_(f)(z)=2·A ^(T) ·K·(A·f−p _(c))+β·R·f  (5′)

In the iteration according to equation (1) the current image data f istherefore convoluted with the regularization convolutional kernel Raccording to equation (5′) every time, and in the frequency domain thiscorresponds to multiplication by the factor M_(R). If, therefore, M_(R)has been determined for example with equation (10), then it is onlynecessary to apply a Fourier transformation to the image data f,multiply the result by the factor M_(R) and subsequently carry out aninverse two-dimensional Fourier transformation to practically implementconvolution of the image data. This ensures that the desired convolutionis carried out in the position space according to the regularizationconvolutional kernel R determined by the target convolutional kernel W.

FIG. 2 schematically shows the structure and the mode of operation ofthe iteration loop in the iterative reconstruction unit 40.

Firstly initial image data f_(k=0) is generated from the incoming inputprojection data p_(c). The image data f_(k) within the iterativereconstruction unit 40 is shown top right in the figure. Syntheticprojection data p_(s,k=0) is generated from this initial image dataf_(k=0) with the aid of a projection operation A in a forward projection(in function block 45). What is known as the Josephson projector can beused here by way of example. This calculates the linear integrals alongpencil beams. The difference between this synthetic projection dataΔp_(s,k=0) and the measured input projection data p_(c) is then formedin a combination operator 41. The result is the difference projectiondata Δp_(k). This difference projection data Δp_(k) is back projected infunction block 42 with the operator A^(T) adjoint to projection operatorA to calculate a residue image, i.e. to determine residue image dataΔf_(k=0). The residue image data Δf_(k=0) is then (optionally with priorweighting with the above-described multiplier α in function block 46)used within a combination unit 43 to update the initial image dataf_(k=0) and thus generate the image data of the next iterative stepΔf_(k=1).

According to an embodiment of the invention the image data f_(k=0) ofthe current iterative step k=0 is processed in advance in a filter unit44 with a low-pass filter operator. This filtering takes place, asalready described above in connection with the mathematical basis of theiteration loop, while taking account of a regularization convolutionalkernel R which is determined on the basis of a target convolutionalkernel W selected in advance for the respective reconstruction.

The target convolutional kernel W can be selected by means of the targetconvolutional kernel selection unit 50 in this case. This may, forexample, be a software module within a user interface of the controlleror image processing device on which the entire reconstruction is carriedout. By way of example, as has previously been conventional, a user canbe offered a large number of suitable convolutional kernels in a menuand he then selects the appropriate target convolutional kernel W on thebasis of his experiences and requirements. The desired kernel W can alsobe indicated by direct selection of image characteristics which havebeen brought about by the desired kernel. This target convolutionalkernel W is then passed to a regularization convolutional kernelcalculation unit 49 which calculates the regularization convolutionalkernel R in the manner described within the framework of themathematical descriptions. Reference is again made at this point to thefact that calculation of the regularization convolutional kernel R isalso taken to mean calculation of the associated factor M_(R) by whichthe image data can be multiplied for filtering in the frequency domain,instead of carrying out convolution with the corresponding convolutionalkernel directly in the position space. Calculation of the factorrespectively associated with the convolutional kernel should thereforebe equated with calculation of the convolutional kernel itself withinthe scope of an embodiment of the invention.

The loop where k=1 is then run through again to generate image dataf_(k=2) in the next iterative step k=2, etc.

Explicit reference is also made to that fact that the loop in FIG. 2 canalso be half run through once for initial construction of the firstimage data f_(k=0) from the beam hardening-corrected projection datap_(c). Initially no synthetic projection data p_(s,k) exists, i.e.p_(s,k) can be equated to 0. The difference projection data Δp_(k)therefore corresponds to the input projection data p_(c), so the residueimage data Δf_(k) ultimately already corresponds to the desired initialimage data which, with suitable selection of the factor α, can bedirectly accepted in the loop as initial image data f_(k=0) in the firststep. Consequently only the factor α=1 has to be set during the firstrun of the loop. Alternatively it is also possible, by avoiding theloop, to generate the initial image data from the input projection datap_(c) and to start the loop at f_(k) (top right in block 40 of FIG. 4).

The iteration loop is continued up until a predefined terminationcriterion. The easiest way is termination following a certain number ofiterative passes. An inquiry of this kind can be made, for example, inthe inquiry function block 47 by comparing the iteration controlvariable k with a maximum value k_(max). If this value is attained, theimage data f is output, otherwise the loop is run though again. Checkingof the residue image is also possible as an alternative or in addition.This variant is shown as inquiry function block 48. Here it is checkedwhether the current residue image Δf_(k) is below a limiting value thatis to be defined in a suitable manner. If yes it is assumed that theconvergence is sufficiently far advanced and the current image data, orimage data updated in the next pass, can be output as complete imagedata f. Further termination criteria are also conceivable, for examplechecking for whether the difference projection data Δp_(k) is below acertain limiting value Δp_(G).

FIG. 3 shows an example embodiment of an image reconstruction device 21with a beam hardening correction unit 30, connected upstream of theinventive iterative reconstruction unit 40, and the input and outputdata for the individual components of this image reconstruction device21. At the start the image reconstruction device 21 comprises aprojection measured data interface 25. This accepts the projectionmeasured data p_(M) and from there it is passed to a beam hardeningcorrection unit 30 which will be described hereinafter with reference toFIG. 4. The projection measured data p_(M) is firstly beamhardening-corrected in the beam hardening correction unit 30 to thusgenerate the input projection data p_(c) for the downstream iterativereconstruction unit 40. The image data f generated by using the targetconvolutional kernel W selected with the target convolutional kernelselection unit 50 is then stored in a memory 22 for example via an imagedata interface 26 and can be retrieved from there again for furtherprocessing, to generate for example certain section images from volumeimage data or the like.

FIG. 4 describes the beam hardening correction unit 30 in slightly moredetail. The accepted projection measured data p_(m) is firstly passed inthe beam hardening correction unit 30 to a correction unit 35, whosefunction will be described later, and secondly to an interim imagereconstruction unit 31. Interim image data f_(I) is reconstructed inthis interim image reconstruction unit 31 by applying a conventionalback projection convolution, the data then being passed to a segmentingunit 32. The bone structures, for example, are segmented in thissegmenting unit 32 and a bone image or bone image data f_(b) is thusgenerated. This segmenting can take place using a conventionalsegmenting method. Instead of bone material, segmenting of otherspecific material, for example vessels filled with contrast medium, canalso take place here if this is expedient in the context of the imagesto be evaluated. In this regard the bone image data f_(b) can also begenerally designated first structure image data f_(b).

The bone image data f_(b) is accepted by a projection data generatingunit 33 which, with a forward projection operator that optimally depictsthe measuring method in mathematical terms, generates bone structureprojection data p_(b) (more generally: first structure projection datap_(b)) therefrom. This is likewise passed to the correction unit 35.

The correction mechanism in the correction unit 35 uses the boneprojection data P_(b) and the projection measured data P_(m) todetermine with the aid of a look-up table LUT, which is stored in amemory 34, water projection data for monochromatic radiation, i.e. beamhardening-corrected water projection data p_(w). This takes place in aprojection data determining unit 36. The water projection data p_(w) isthen combined, for example simply added, with/to the bone projectiondata p_(b) in the combination unit 37 to generate the desired beamhardening-corrected projection data p_(c) therefrom.

The entire correction mechanism is based on the assumption that thegenerated bone projection data p_(b) can be adopted asquasi-monochromatic data, i.e. beam hardening-corrected data. Thelook-up table LUT was determined in advance with the aid of testmeasurements or simulations. Polychromatic X-ray radiation is radiatedvertically through a wedge arrangement in the process and polychromaticline integrals, i.e. projection values, are thus generated for water andbone, for example, as a function of the radiographed water and bonethicknesses. The water thickness may in turn be derived from thesematerial combination-dependent, polychromatic line integrals and thebone thickness. The bone thickness, however, can in turn be inverselyderived from the bone projection data p_(b). Monochromatic waterprojection values p_(w) may therefore be determined which then only haveto be mixed with the quasi-monochromatic bone projection values p_(b) toobtain quasi-monochromatic, beam hardening-corrected overall projectionvalues p_(c) which can then be used as input projection values p_(c) forthe iterative reconstruction. Water is used as an equivalent to softtissue in this connection. A

similar method is used in DE 100 36 142 B4 (the entire contents of whichare hereby incorporated herein by reference) where, however, thecorrection is applied to completely reconstructed images from whichsectional images are generated respectively to thus reconstruct thecomplete image. With regard to the basic mathematical and physicalprinciples for the measuring setup and for the simulation fordetermining the look-up table LUT, reference can be made to thestatements in this document, however. A separation of soft tissue andbone material is assumed in the present explanations. The method can,however, also be used with other materials, for example soft tissue andcontrast medium. It is only necessary to use a look-up table LUTaccordingly determined therefor in this case.

FIG. 5 shows a variant for a filter device 44′ with which furtherimprovement in image quality is possible within the iterativereconstruction. This principle is based on the fact that filtering isnot carried out in the same way for all materials in the image, insteadthe image data f_(k) of the current iterative stage, which is combinedwith the residue image data within the combination unit 43 to generatethe image data of the next iterative step, is firstly segmented in aseparating unit 51 to generate different material type image data f_(k)¹, f_(k) ². The first material type image data f_(k) ¹ is, for example,a bone image, i.e. a high-contrast image. The second material type imagedata f_(k) ² is low-contrast soft tissue image on the other hand. Theseparating unit 51 can operate like a conventional segmenting unit inthis case, as has also already been described in FIG. 4 in connectionwith the beam hardening correction.

The different material type image data f_(k) ¹, f_(k) ² is thenseparately filtered in each case and, more precisely, using first andsecond regularization convolutional kernels R₁, R₂ appropriate to therespective image data. This is symbolized in FIG. 5 by the separatefilter function blocks 52, 53. The convolutional kernels R₁, R₂ canagain be calculated for example by the regularization convolutionalkernel calculation unit 49 (see FIG. 2) from the target convolutionalkernel W selected by the operator. Furthermore, filtering proceedsmathematically in exactly the same way as when the complete update imagef_(k) is filtered in the manner according to the invention. This imagedata f_(k) ¹, f_(k) ² can again be mixed in a mixing unit 54 to from acommon image f_(k)′. It is also possible for mixing to take place withthe original update image f_(k). The mixing unit can also be configuredin this connection such that the different image data f_(k) ¹, f_(k) ²is not added up again with the same weight, rather a weighting of theindividual image data f_(k) ¹, f_(k) ² is carried out to ensure that onetype of image data dominates. In particular this may also be local—i.e.pixel-dependent—weighting in which it is decided which image data ismore strongly weighted for each pixel. The image data generated by themixer is then combined with the residue image data in the combinationunit 43 instead of as a combination image f_(k)′. The mixing unit 54can, moreover, be constructed with the combination unit 43 as anintegrated unit.

In a further variant filtering is only carried out with a regularizationfiler kernel for one of the image data material types, while the imagedata of the other type of material is used unfiltered. By way ofexample, soft tissue image data only can be filtered and thehigh-contrast data, which is generated by the bone structure, is usedagain in the updating of the image data f_(k) in the iteration withoutlow-pass filtering, i.e. without using a regularization kernel, toretain the strong contrast in the process. As an example of this FIG. 6shows a bone image (left-hand side) generated from a section imagethrough a head and the associated soft tissue image (right-hand side),which has been filtered with a certain regularization filter. Suchseparate treatment of different image structures and recombination afterfiltering means it is possible to have an even greater influence on theimage characteristics in order to generate optimum images.

The components shown in FIGS. 2 to 5 can be predominantly or whollyimplemented on a suitable processor in the form of software elements. Inparticular the interfaces between these components may also beconstructed purely in terms of software. It is only necessary for thereto be access measures to suitable memory areas in which the data can besuitably buffered and retrieved and updated again at any time.

The method and the construction device have primarily been describedwith reference to a reconstruction of medical image data. The inventionis not restricted to use in the medical sector, however, instead CTscans may, in principle, also be generated and processed for otherpurposes, for example for material testing or the like.

Finally reference is again made to the fact that the above-describedmethod and devices are example embodiments of the invention, and thatthe invention may be varied by a person skilled in the art withoutdeparting from the field of the invention as far as it is specified bythe claims. For the sake of completeness reference is also made to thefact that the use of the indefinite article “a” does not rule out therelevant features from also existing severalfold. Similarly, the term“unit” or “module” does not rule out these comprising a plurality ofcomponents which may optionally also be spatially scattered.

The patent claims filed with the application are formulation proposalswithout prejudice for obtaining more extensive patent protection. Theapplicant reserves the right to claim even further combinations offeatures previously disclosed only in the description and/or drawings.

The example embodiment or each example embodiment should not beunderstood as a restriction of the invention. Rather, numerousvariations and modifications are possible in the context of the presentdisclosure, in particular those variants and combinations which can beinferred by the person skilled in the art with regard to achieving theobject for example by combination or modification of individual featuresor elements or method steps that are described in connection with thegeneral or specific part of the description and are contained in theclaims and/or the drawings, and, by way of combinable features, lead toa new subject matter or to new method steps or sequences of methodsteps, including insofar as they concern production, testing andoperating methods.

References back that are used in dependent claims indicate the furtherembodiment of the subject matter of the main claim by way of thefeatures of the respective dependent claim; they should not beunderstood as dispensing with obtaining independent protection of thesubject matter for the combinations of features in the referred-backdependent claims. Furthermore, with regard to interpreting the claims,where a feature is concretized in more specific detail in a subordinateclaim, it should be assumed that such a restriction is not present inthe respective preceding claims.

Since the subject matter of the dependent claims in relation to theprior art on the priority date may form separate and independentinventions, the applicant reserves the right to make them the subjectmatter of independent claims or divisional declarations. They mayfurthermore also contain independent inventions which have aconfiguration that is independent of the subject matters of thepreceding dependent claims.

Further, elements and/or features of different example embodiments maybe combined with each other and/or substituted for each other within thescope of this disclosure and appended claims.

Still further, any one of the above-described and other example featuresof the present invention may be embodied in the form of an apparatus,method, system, computer program, computer readable medium and computerprogram product. For example, of the aforementioned methods may beembodied in the form of a system or device, including, but not limitedto, any of the structure for performing the methodology illustrated inthe drawings.

Even further, any of the aforementioned methods may be embodied in theform of a program. The program may be stored on a computer readablemedium and is adapted to perform any one of the aforementioned methodswhen run on a computer device (a device including a processor). Thus,the storage medium or computer readable medium, is adapted to storeinformation and is adapted to interact with a data processing facilityor computer device to execute the program of any of the above mentionedembodiments and/or to perform the method of any of the above mentionedembodiments.

The computer readable medium or storage medium may be a built-in mediuminstalled inside a computer device main body or a removable mediumarranged so that it can be separated from the computer device main body.Examples of the built-in medium include, but are not limited to,rewriteable non-volatile memories, such as ROMs and flash memories, andhard disks. Examples of the removable medium include, but are notlimited to, optical storage media such as CD-ROMs and DVDs;magneto-optical storage media, such as MOs; magnetism storage media,including but not limited to floppy disks (trademark), cassette tapes,and removable hard disks; media with a built-in rewriteable non-volatilememory, including but not limited to memory cards; and media with abuilt-in ROM, including but not limited to ROM cassettes; etc.Furthermore, various information regarding stored images, for example,property information, may be stored in any other form, or it may beprovided in other ways.

Example embodiments being thus described, it will be obvious that thesame may be varied in many ways. Such variations are not to be regardedas a departure from the spirit and scope of the present invention, andall such modifications as would be obvious to one skilled in the art areintended to be included within the scope of the following claims.

LIST OF REFERENCE CHARACTERS

-   1 computerized tomography system-   2 upper part of the patient couch-   3 patient couch-   5 detector system-   10 scanner-   11 gantry-   12 measuring chamber-   15 x-ray source-   16 detector-   20 controller-   21 image reconstruction device-   22 memory-   23 raw data interface-   24 control interface-   25 projection measured data interface-   26 image data interface-   30 beam hardening correction unit-   31 interim image reconstruction unit-   32 segmenting unit-   33 projection data generating unit-   34 memory-   35 correction unit-   36 projection data determining unit-   37 combination unit-   40 iterative reconstruction unit-   41 combination operator-   42 function block-   43 combination unit-   44 filter unit-   44′ filter unit-   45 function block-   46 function block-   47 inquiry function block-   48 inquiry function block-   49 regularization convolutional kernel calculation unit-   50 target convolutional kernel selection unit-   51 separating unit-   52 filter function block-   53 filter function block-   54 mixer unit-   AS acquisition control signal-   O object/patient-   z system axis-   f image data-   f_(k=0) initial image data-   f_(I) interim image data-   f_(b) first structure image data/bone image data-   p_(m) projection measured data-   p_(c) beam hardening-corrected projection data-   p_(b) first structure projection data/bone structure projection data-   p_(w) second structure projection data/water projection data-   p_(s,k) synthetic projection data-   A projection operator-   A^(T) adjoint projection operator-   Δf_(k) residue image data-   Δf_(k) limiting value-   Δp_(k) difference projection data-   Δp_(G) limiting value-   α multiplier-   k iterative control variable-   k_(max) maximum value-   LUT correction data table/look-up table-   f_(k) ¹ first material type image data-   f_(k) ² second material type image data-   f_(k)′ combination image-   R regularization convolutional kernel-   R₁ first regularization convolutional kernel-   R₂ second regularization convolutional kernel

1. A method for reconstructing image data on the basis of inputprojection data obtained via an X-ray computerized tomography system,wherein a target convolutional kernel is selected and wherein image dataare then reconstructed using an iterative reconstruction methodcomprising: reconstructing image data of a first iterative stage fromthe input projection data; generating synthetic projection data on thebasis of the image data of a current iterative stage; forming differenceprojection data on the basis of the input projection data and thesynthetic projection data; generating residue image data from thedifference projection data; combining the residue image data with theimage data of the current iterative stage to form image data of anadditional iterative stage, wherein the image data of the currentiterative stage is subjected to filtering before or during combinationwith the residue image data by using a regularization convolutionalkernel which is determined on the basis of the selected targetconvolutional kernel; and repeating the generating synthetic projectiondata, forming difference projection data, generating residue image dataand combining until a termination condition occurs.
 2. The method asclaimed in claim 1, wherein the regularization convolutional kernel isdetermined by taking account of a projection operator used whengenerating the synthetic projection data.
 3. The method as claimed inclaim 2, wherein the residue image data is generated from the differenceprojection data by way of a filtered back projection method using a backprojection convolutional kernel, and wherein the regularizationconvolutional kernel is determined by taking account of the backprojection convolutional kernel.
 4. The method as claimed in claim 3,wherein a RamLak kernel or a Shepp-Logan kernel is used as the backprojection convolutional kernel.
 5. The method as claimed in claim 1,wherein the residue image data is generated from the differenceprojection data by way of a filtered back projection method using a backprojection convolutional kernel, and wherein the regularizationconvolutional kernel is determined by taking account of the backprojection convolutional kernel.
 6. The method as claimed in claim 5,wherein a RamLak kernel or a Shepp-Logan kernel is used as the backprojection convolutional kernel.
 7. The method as claimed in claim 1,wherein separate material type image data for different types ofmaterial is generated from the image data of the current iterativestage, before combination with the residue image data, the material typeimage data is subjected to separate filtering by using a regularizationconvolutional kernel determined for the respective type of material andis then combined with the residue image data.
 8. The method as claimedin claim 7, wherein local weighting takes place when combining thematerial type image data with the residue image data.
 9. The method asclaimed in claim 1, wherein iteration is terminated at the latest aftera number of iterative steps.
 10. The method as claimed in claim 1,wherein iteration is terminated at the latest when a convergencecriterion is reached.
 11. The method as claimed in claim 1, wherein theinput projection data is obtained on the basis of projection measureddata acquired via an X-ray computerized tomography system, the datafirstly being subjected to a beam hardening correction.
 12. The methodas claimed in claim 11, wherein, for the beam hardening correction,interim image data is firstly reconstructed from the projection measureddata and first structure image data of a first type of material issegmented from this interim image data and first structure projectiondata is generated on the basis of the first structure image data and thebeam hardening-corrected projection data is determined on the basis ofthe first structure projection data and the projection measured data.13. A method for generating image data from inside an object by way ofan X-ray computerized tomography system, the method comprising: X-rayingthe object using X-ray radiation from a plurality of projectiondirections to acquire projection measured data; and reconstructing theimage data on the basis of the projection measured data using the methodas claimed in claim
 11. 14. An image reconstruction device forreconstructing image data of an object, comprising: a projection datainterface to accept input projection data obtained via an X-raycomputerized tomography system; a target convolutional kernel selectionunit to select a target convolutional kernel; and an iterativereconstruction unit, adapted to reconstruct image data based on theinput projection data using an iterative reconstruction method,comprising: reconstructing image data of a first iterative stage fromthe input projection data, generating synthetic projection data on thebasis of the image data of a current iterative stage, forming differenceprojection data on the basis of the input projection data and thesynthetic projection data, generating residue image data from thedifference projection data, combining the residue image data with theimage data of the current iterative stage to form image data of anadditional iterative stage, wherein the image data of the currentiterative stage is subjected to filtering before or during combinationwith the residue image data by using a regularization convolutionalkernel determined on the basis of the selected target convolutionalkernel, and repeating the generating synthetic projection data, formingdifference projection data, generating residue image data and combininguntil a termination condition occurs; and an image data interface tooutput the reconstructed image data.
 15. The image reconstruction deviceas claimed in claim 14, wherein the iterative reconstruction unitcomprises a filter unit including a separating unit, adapted to generateseparate material type image data for different types of material fromthe image data of the current iterative stage, before combination withthe residue image data, and to separately filter the material type imagedata by using a regularization convolutional kernel determined for therespective type of material, in order to then combine the material typeimage data with the residue image data.
 16. An X-ray computerizedtomography system comprising: a projection data acquisition unit; anX-ray source; a detector system to acquire projection measured data ofan object; and the image reconstruction device as claimed in claim 14.17. An X-ray computerized tomography system comprising: a projectiondata acquisition unit; an X-ray source; a detector system to acquireprojection measured data of an object; and the image reconstructiondevice as claimed in claim
 15. 18. A non-transitory computer readablemedium including program segments for, when executed on a programmableimage reconstruction device, causing the programmable imagereconstruction device to implement the method of claim 1.